Job shop scheduling with a genetic algorithm and machine learning

Lee, Chung Yee; Piramuthu, Selwyn; Tsai, Yi Lin

doi:10.1080/002075497195605

Cited by 136 publications

(62 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of jobs to be completed per day can be interpreted as the load on a factory. It is well established in the literature that dierent levels of work loads on a factory require dierent scheduling heuristics to minimize the same objective function [25].…”

Section: Experimental Investigationmentioning

confidence: 99%

Scheduling with alternatives: a link between process planning and scheduling

Weintraub¹,

Cormier²,

Hodgson³

et al. 1999

IIE Transactions

View full text Add to dashboard Cite

The objective of this research is to develop and evaluate eective, computationally ecient procedures for scheduling jobs in a large-scale manufacturing system involving, for example, over 1000 jobs and over 100 machines. The main performance measure is maximum lateness; and a useful lower bound on maximum lateness is derived from a relaxed scheduling problem in which preemption of jobs is based on the latest ®nish time of each job at each machine. To construct a production schedule that minimizes maximum lateness, an iterative simulation-based scheduling algorithm operates as follows: (a) job queuing times observed at each machine in the previous simulation iteration are used to compute a re®ned estimate of the eective due date (slack) for each job at each machine; and (b) in the current simulation iteration, jobs are dispatched at each machine in order of increasing slack. Iterations of the scheduling algorithm terminate when the lower bound on maximum lateness is achieved or the iteration limit is reached. This scheduling algorithm is implemented in Virtual Factory, a Windows-based software package. The performance of Virtual Factory is demonstrated in a suite of randomly generated test problems as well as in a large furniture manufacturing facility. To further reduce maximum lateness, a second scheduling algorithm also incorporates a tabu search procedure that identi®es process plans with alternative operations and routings for jobs. This enhancement yields improved schedules that minimize manufacturing costs while satisfying job due dates. An extensive experimental performance evaluation indicates that in a broad range of industrial settings, the second scheduling algorithm can rapidly identify optimal or nearly optimal schedules.

show abstract

Section: Experimental Investigationmentioning

confidence: 99%

Scheduling with alternatives: a link between process planning and scheduling

Weintraub¹,

Cormier²,

Hodgson³

et al. 1999

IIE Transactions

View full text Add to dashboard Cite

show abstract

“…GAs are stochastic search methods designed to search large and complex spaces by exploitation of currently known solutions and a robust exploration of the space (Lee et al, 1997). GAs start with a collection (or population) of randomly selected solutions (or individuals).…”

Section: Hybrid Ga Approachmentioning

confidence: 99%

Minimizing the Total Stretch in Flow Shop Scheduling

Yoon

2014

Management Science and Financial Engineering

View full text Add to dashboard Cite

A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flow shop scheduling. HGA adopts the idea of seed selection and development in order to reduce the chance of premature convergence that may cause the loss of search power. The performance of HGA is compared with that of genetic algorithms (GAs).

show abstract

“…Some of the typical applications of GAs includes: Traveling salesman problem (Grefenstette et al, 1985); Scheduling problem (Davis, 1985;Cleveland and Smith, 1989); VLSI Circuit layout design problem (Fourman, 1985); Computer aided gas pipeline operation problem (Goldberg, 1987a(Goldberg, , 1987b; Communication network control problem (Cox et al, 1991); Real time control problem in manufacturing systems (Lee et al, 1997) etc. The efficient implementation of GA over a problem requires following:…”

Section: Genetic Algorithmmentioning

confidence: 99%

An Intelligent Decision Support System for Workforce Forecast

Wan¹,

Chen²,

Kuriger³

2011

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) 05-01-2011 REPORT TYPE 7184D214 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERThe University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Air SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-RH-WP-TR-2011-0019 DISTRIBUTION / AVAILABILITY STATEMENTDistribution A: Approved for public release; distributed is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared on 23 Feb 2011, 88ABW-2011-0727. ABSTRACTThe main objective of this project is to enhance the effectiveness of workforce forecast and deployment through innovative approaches of artificial intelligence. The research included a final document of conventional and innovative methods of workforce forecasting and a decision support software program incorporating advanced artificial intelligence techniques for workforce forecasting. SUBJECT TERMS ABSTRACTThis project studies workforce forecasting in two main aspects: (1) an extensive review of the existing methodologies and techniques and (2) an effort to develop a decision support system with models and software programming. The main objective of this project is to enhance the effectiveness of workforce forecasting and deployment through innovative approaches of artificial intelligence. The deliverables include a summary report of conventional and innovative methods of workforce forecasting (Part I) and a decision support software program using artificial intelligence techniques for workforce forecasting (Part II).Part I, provided a thorough literature review of fundamental research and practices of demand and supply forecasting techniques for workforce analysis. Over 300 relevant literatures have been identified and reviewed by the project team, and 289 of them are covered in this report.Following is a summary of the main contents and contributions of Part I:• This report provides an extensive review of the fundamental knowledge, applications, guidelines, and scope of use of various workforce forecasting methods.• A decision tree has been proposed for selecting an appropriate forecasting ...

show abstract

Job shop scheduling with a genetic algorithm and machine learning

Cited by 136 publications

References 0 publications

Scheduling with alternatives: a link between process planning and scheduling

Scheduling with alternatives: a link between process planning and scheduling

Minimizing the Total Stretch in Flow Shop Scheduling

An Intelligent Decision Support System for Workforce Forecast

Contact Info

Product

Resources

About